by Khan, W and Klette, R
Abstract:
Feature-point tracking for the purpose of object tracking in a driver-assistance context is not an easy task. First, to track rigid objects, feature points have to be matched frame-by-frame and then, by using disparity maps, their real-world position can be derived, from which the object velocity is estimated. Unfortunately, a feature-point matcher cannot find (reliable) matches in all frames. In fact, the performance of a matcher varies with the type of feature-point detector and descriptor used. Our comparison of different feature-point matchers gives a general impression of how descriptor performance degrades as a rigid object approaches the ego-vehicle in a collision-scenario video sequence. To handle the mismatches, we use a Kalman-filter-based tracker for each tracked feature point. The tracker with the maximum number of matches and with a most recent match is chosen as the optimal tracker. The role of the optimal tracker is to assist in updating the tracker of a feature point which had no match. The optimal tracker is also used in estimating the object velocity. To understand the behaviour of the safety system, we used the DoG detector in combination with SURF, BRIEF, and FREAK descriptors, while linBP and iSGM are used as stereo matchers. The novelty in our work is the performance evaluation of a stereo-based collision avoidance system (avoidance by brake warning) in a real collision scenario.
Reference:
Accuracy of trajectories estimation in a driver-assistance context (Khan, W and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, volume 8334, 2014.
Bibtex Entry:
@inproceedings{khan2014accuracycontext,
author = "Khan, W and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "47--58",
publisher = "Springer Verlag",
title = "Accuracy of trajectories estimation in a driver-assistance context",
volume = "8334",
year = "2014",
abstract = "Feature-point tracking for the purpose of object tracking in a driver-assistance context is not an easy task. First, to track rigid objects, feature points have to be matched frame-by-frame and then, by using disparity maps, their real-world position can be derived, from which the object velocity is estimated. Unfortunately, a feature-point matcher cannot find (reliable) matches in all frames. In fact, the performance of a matcher varies with the type of feature-point detector and descriptor used. Our comparison of different feature-point matchers gives a general impression of how descriptor performance degrades as a rigid object approaches the ego-vehicle in a collision-scenario video sequence. To handle the mismatches, we use a Kalman-filter-based tracker for each tracked feature point. The tracker with the maximum number of matches and with a most recent match is chosen as the optimal tracker. The role of the optimal tracker is to assist in updating the tracker of a feature point which had no match. The optimal tracker is also used in estimating the object velocity. To understand the behaviour of the safety system, we used the DoG detector in combination with SURF, BRIEF, and FREAK descriptors, while linBP and iSGM are used as stereo matchers. The novelty in our work is the performance evaluation of a stereo-based collision avoidance system (avoidance by brake warning) in a real collision scenario.",
isbn = "9783642539251",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
conference = "PSIVT 2013 Workshops",
}